Oct 2024 – Present · 1 yr 5 mos · San Francisco Bay Area · Remote

• Advocate for products and services pertaining to AI, generative AI and LLMs. Featuring curated Nvidia content in my GenAI newsletter (200k subscribers) and other media, presenting at selected Nvidia events. Deep understanding of the AI landscape and technology, building ground-breaking architecture known as LLM 2.0 / xLLM, with our startup BondingAI.io participating in the Nvidia Inception program.

Apr 2024 – Sep 2024 · 5 mos · São Paulo, Brazil · Remote

• Helping Fortune 100 company InBev implement my xLLM technology as an alternative to OpenAI and LangChain to boost relevancy (hallucination-free), reduce latency, and increase speed by several orders of magnitude, while increasing security via sub-LLMs accessible to authorized users only. Offering a better solution for search and cataloging, to help users navigate their corporate repository. With a focus on concise but exhaustive results (via augmentation) and specialized sub-LLMs. Local implementation without external API calls.

Sep 2023 – Oct 2025 · 2 yrs 1 mo · Seattle, Washington, United States · Remote

• Now part of the BondingAI network. Building better, faster, and less expensive GenAI products with explainable AI, such as NoGAN (data synthesizer). Key product: xLLM, a fast in-memory agentic multi-LLM system built from scratch, with zero weight, no training, no latency, no hallucination, fully contextual from the ground up, with real-time fine-tuning. For scientific or enterprise search, as well as clustering and predictions. Also, builder of the best model evaluation metric on the market.

Apr 2019 – Jan 2024 · 4 yrs 9 mos · Issaquah, WA

• AI optimization research lab, developing state-of-the-art technology with a focus on high quality and fast algorithms, thus cost effective. Our GenAI Web APIs are accessible on GenAItechLab.com. Covering synthetic tabular data, artificial DNA sequences, time series & geospatial data generation, agent-based modeling, synthetic graphs, and customized LLMs.

Jan 2012 – Jun 2012 · 5 mos · Bellevue, Washington, United States

• Developed a strategy to collect the right data and detect minuscule change points in time series associated with KPI's (when the changes occur, their amplitudes and directions). Goal: assessing the impact of events influencing core metrics. Example: changing the definition of a website user resulting in shift in user count, or changing some features on Bing.com resulting in increase / decrease in pageviews. These were blind tests: I did not know when the website changes were made. But when I detected a change, it was matched against known events that took place at the time flagged by my algorithm as "change point". I designed and then helped automate this algorithm.

Apr 2010 – Mar 2012 · 1 yr 11 mos · San Francisco Bay Area

• Optimizing advertising campaigns, Ad arbitrage. Projects and technology include:
• Created list of top commercial keywords accounting for 85% of all pay-per-click revenue
• Leveraged Google API's to automatically find millions of high yield keywords
• Automated taxonomy creation (NLP)
• Click fraud detection (click jacking and Botnet detection)
• Internet traffic quality scoring using hidden decision trees
• Automation of keyword bidding
• Reverse engineering Google keyword pricing algorithms, for ad arbitrage

Sep 2009 – Dec 2009 · 3 mos · San Jose, California, United States

• Keyword pricing optimization. Improving keyword bid pricing algorithm to update bids on millions of keywords purchased on Google AdWords. Automated daily updates. Setting bids for large tail of keywords with no conversion history, based on aggregation methods (NLP). Detection of new keywords.

May 2008 – Mar 2020 · 11 yrs 10 mos · Greater Seattle Area

• The company was acquired in 2020. I am still a contributing author.

May 2008 – Aug 2009 · 1 yr 3 mos · Austin, Texas, United States

• Built scoring system to measure traffic quality associated to keywords and publishers, for keyword ad networks, search engines, or advertisers (the customers). Scores were used for fraud detection, removal of bad publishers and keyword pricing. Detected major Botnets. Developed proofs of concept, attracting Looksmart and eBay as customers.

Jun 2006 – May 2008 · 1 yr 11 mos · Greater Los Angeles Area

• VC-funded startup. Prototyped automated bidding and click/keyword scoring solutions for search engines, ad networks and advertisers. Worked with the engineering team to implement HDT (hidden decision tree) algorithms. Worked on score standardization and IP blacklist/whitelist architecture. Developed patent-pending statistical technologies and other intellectual properties. Presented at Ad-Tech and the American Statistical Association conferences. Helped win several deals (Microsoft, Turn.com) against large competitors (including Fair Isaac), through proof-of-concept blind tests. Identified major Botnet.

Jan 2005 – Jun 2006 · 1 yr 5 mos · Greater Seattle Area

• Click scoring technology, query intelligence, web analytics, business intelligence related to mobile search. Designed click fraud detection algorithms to process billions of clicks. Created rule selection and rule discovery system for fraud detection, based on machine learning (unsupervised clustering), design of experiments, robust cross validation and linkage with external data sources (Google search results) to discover additional fraud patterns. Enhanced keyword taxonomies using data driven algorithms to detect keyword associations. Defined and tested metrics for keyword correlations. Developed multi-threaded web crawler to feed text mining algorithms with rich, targeted data sources related to local search and / or yellow pages.

Jun 2004 – Nov 2004 · 5 mos · San Francisco Bay Area

• Detected abnormalities in traffic monitoring systems resulting in a major fix in the Tealeaf reporting system. The bug resulted in visitors and web session statistics to be wrong for all big vendors relying on multiple servers, with visitor session data collected across multiple servers broken down by error in multiple sessions / multiple users.

Feb 2003 – May 2004 · 1 yr 3 mos · San Francisco Bay Area

• Developed proprietary feature selection system (200x faster than SAS Enterprise Miner) to detect first instances of fraud and horizontal (single ping) fraud in real time. Production of US zip code maps showing fraud simultaneously in the time, location, recency and volume dimensions. SAS, Perl, C, R, Splus.

Jun 1996 – May 2002 · 5 yrs 11 mos · Greater New York City Area

• Web analytics. Attribution modeling using time series techniques. Inventory forecasting (NBCi). Price elasticity modeling. Web scraping. Advertising mix optimization for both NBC Internet (NBCi) and CNET. Customer profiling. User retention, churn, survival models. Data Warehousing. Web traffic forecast (with automated alarm system to notify product managers of traffic abnormalities). Automating production of various reports (dashboard, quarterly reports for financial analysts). KPI design.

Jul 1995 – Jun 1996 · 11 mos · Raleigh-Durham, North Carolina Area

• Environmental statistics. MCMC. Hierarchichal Bayesian models. Clustering. Storm modeling (time series, spatial processes). Hanford nuclear reservation: risk analysis using space / time models to detect radioactive leakage into the nearby Columbia river (funded by the EPA.) Simulation of bivariate exponential distributions.